1. Technical Field
The present disclosure relates to a wireless hardware device for detecting relations of distance, in particular relations of proximity, with respect to other similar wireless hardware devices, and to a system for monitoring relations of distance between wireless hardware devices. The ensuing disclosure will make particular reference, without this implying any loss of generality, to the use of this wireless hardware device and monitoring system for high-resolution dynamic mapping of interactions between individuals in a given context of social aggregation.
2. Description of the Related Art
As is known, in the last few years there has arisen an increasing interest in the study of the patterns of social interaction, aimed at obtaining a greater understanding on the phenomena of creation and evolution of the contacts occurring between a number of individuals. An understanding of these phenomena can prove of fundamental importance, for example, for forecast and control of the spread of contagion due to infectious illnesses, which occurs following upon the so-called direct or respiratory contact, or else for monitoring and control of processes of formation of public opinion, or also for creation and management of mobile wireless networks in environments in which access to network infrastructures cannot be guaranteed.
Even though a detailed characterization of these phenomena is certainly required for reaching an analytical understanding thereof, up to now it has been difficult to obtain representative empirical data on which to build such a characterization. In particular, many of the available studies have prevalently concentrated on static characterizations of the models of interaction between individuals, neglecting important properties that occur in the time dimension, amongst which the duration, frequency, simultaneity, and causality of the contacts.
More recently, there has been proposed the use of modern wireless communications technologies, amongst which Wi-fi, Bluetooth or infrared (IR) technology, to carry out collection of data both on the structural and on the temporal aspects of the patterns of social interaction. For this purpose, there has been proposed the use of a plurality of wireless devices carried by individuals during mutual interactions, and of techniques for detecting the mutual positions and distances of the various wireless devices, on the basis of which it will be possible to infer the contacts and interactions that occur between the corresponding individuals. In said systems, there is always envisaged a centralized processing (carried out, for example, by a central processing server) of the information of contact detected by the various wireless devices (and transmitted in an appropriate way to the server).
For example, Formo F. et al.: “Design and implementation of a Bluetooth ad hoc network for indoor positioning”, IEEE Proceedings: Software, IEE, Stevenage, GB, vol. 152, no. 5, Oct. 7, 2005, pages 223-228, discloses an architecture for indoor positioning based on Bluetooth sensors. Distance from sensors is estimated using inquiry reports issued at different cyclic power levels, while a centralized positioning system collects data sent by the sensors through an ad-hoc network formed by the same sensors. In particular, each sensor collects responses to the inquiry packets received from other sensors and retransmits these responses to a centralized server together with its identity; the centralized server centrally processes the received responses in order to determine a distance relation between the sensors.
WO 2007/130746 A discloses a system for distance estimation between a central electronic device and one or more remote electronic devices. The central device generates packets of data at different power levels and transmits these data packets to the one or more remote devices. The remote devices receive these data packets and perform a packet loss rate (PLR) computation; the remote devices also transmit to the central device the computation results. The central device receives the computation results from each remote device and centrally performs an estimation of the distance to the various remote devices, based on this computation result; accordingly, a distance between the central device and each remote device may be centrally determined at the central device.
These systems have not, however, proven satisfactory for a series of reasons. In particular, their spatial and temporal resolution has proven unsatisfactory for obtaining reliable information on the phenomena of social interaction; these systems enable detection of the local proximity between the various wireless devices, which, however, not necessarily is indicative of a contact or social interaction having occurred between the individuals that carry said wireless devices.
In addition, the aforesaid systems cannot be easily used for monitoring a large number of individuals, mainly due to the centralized and static nature of the distance processing operations (which are indeed carried out at a central processing server or electronic device).
Recently, the use has also been proposed of RFID (Radio Frequency Identification) devices, the so-called “RFID tags” (in particular of the active type), in the context of detection of the models of social interaction. The RFID tags are each associated to a respective individual, and are designed to be detected by fixed infrastructures uniformly arranged in the detection environment. The RFID tags transmit continuously data packets that are received by the fixed infrastructures and conveyed by these to a central processing server. The latter analyses the data and infers the position of the various RFID tags and the corresponding relations of distance (from which it infers the contacts that have occurred between the individuals).
The reliable location of the environmental position of the RFID tags requires, however, a large number of fixed reception stations, and is subject to errors that cannot be controlled a priori and limiting the spatial and temporal resolution in the detection of the contacts, amongst which the possibility of creation of multiple paths, of phase fluctuations, etc.
In particular, as it is known, a wide range of techniques for determining the mutual position of a transmitter and of a receiver are currently available (that can be used in the aforesaid systems for detecting contacts between individuals). Amongst these techniques there are, for example: techniques based upon the differences in times of arrival (so-called TDoA—Time Difference of Arrival, or E-OTD—Enhanced Observed Time Difference), in which two or more synchronized transmitters transmit data packets, and a receiver calculates the distances on the basis of the observed time differences of arrival of the same data packets; techniques based upon the time of arrival (ToA), in which distances are estimated on the basis of the speed of propagation of the signals; techniques based upon the effective measurement of the strength of the signal received (the so-called RSSI—Received-Signal-Strength Indicator); techniques based upon the carrier-signal phase, which are used for improving the precision of the previous measurement approaches, up to resolutions of less than the wavelength; and techniques based upon the cell of origin, which are based upon the visibility of a transmitter having a unique identifier code.
However, none of above techniques has proven satisfactory in association with the use of RFID tags (or of other wireless devices) in the framework of detection of the social interactions between individuals. In particular, it is known that the strength of the signal received can be measured in a reliable way only by transmitting long data packets, a fact that increases the danger of collision and does not enable scalable deployment of a large number of RFID devices (which is instead necessary for the aforesaid application). In addition, the transmission of long data packets entails a considerable power consumption (and hence does not enable prolonged monitoring operations to be carried out, as would instead be necessary). The estimation of the distances on the basis of the times of arrival requires a synchronization and a common time-base between the various devices, characteristics that are not easy to obtain in environments characterized by a high mobility (such as precisely those inherent in detection of contacts between individuals, amongst which conference centers or centers for other social events, during which the distances and visibility between the various devices change continuously).